Camera Move Desk / API
Get a token

Driving Camera Move Desk from code

Everything the web app does is available over HTTP. The base URL is https://api.skillsafe.ai/v1/app-api, every request carries Authorization: Bearer <token>, and every response is the same envelope.

The task field comes first

This app has five lanes behind one endpoint. Every run body must carry a task field naming the lane - it is what the system prompt routes on. Send the wrong one and you get a valid package of the wrong kind; omit it and the model picks the closest lane and tells you which it chose.

One more shape trap: the run body is the input object. Do not wrap it in an {"input": ...} envelope - that returns 200 while hiding task from the model, which is the most confusing way this API can fail.

taskLaneFieldsSections returned
pickMove selectionbeat, emotion, seconds, shot_type, from_framing, to_framingSummary, Move Shortlist, Ruled Out, Chosen Move, Duration Plan, Next Step
shotShot promptbeat, move, seconds, shot_type, from_framing, to_framing, travel, look, draftSummary, Move Plan, Shot Prompt, Framing Check, Next Step
facePerformance schedulearc, seconds, move, scene, draftSummary, Beat Schedule, Performance Prompt, Limits Check, Next Step
listShot listsequence, envelope, look, must_includeSummary, Shot List, Duration Budget, Coverage Check, Next Step
fixTake triageprompt, symptom, takes, verdict, seconds, shot_typeSummary, Verdict, Diagnosis, One Variable, Revised Prompt, Next Step

Only task, seconds and the lane's own required field are mandatory: beat for pick and shot, arc for face, sequence and envelope for list, and prompt plus symptom for fix. Everything else has a sensible default. seconds, envelope and takes are numbers, not strings - sending "8" where 8 is expected is the most common 400. move must be a name from the catalogue, spelled exactly; the full list is in llms.txt. shot_type is what turns the duration band from a guess into a check, so state it.

Add $model to any body to choose the model for that run: gpt-5.6-luna, gpt-5.6-terra (the default) or gpt-5.6-sol. Luna caps output at 4,096 tokens and will fail the pick, list and fix lanes rather than shorten them.

The response envelope

Success and failure have the same outer shape, so one check covers both.

{
  "ok": true,
  "data": {
    "...": "the result"
  }
}
{
  "ok": false,
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "seconds should be number, got string",
    "details": {}
  }
}
HTTPerror.codeWhat it means
400VALIDATION_ERRORThe body was not a JSON object, or a declared field had the wrong type. A number field sent as a string is the usual cause.
401UNAUTHORIZEDNo token, or a token that has expired or been revoked. Mint a new one.
402INSUFFICIENT_CREDITSThe balance is below the run's minimum. Call /estimate first and compare hold_credits against /me.
404NOT_FOUNDWrong path, or a job id that does not belong to this token.
409CONFLICTAn Idempotency-Key replay whose body differs from the original request.
429RATE_LIMITEDToo many requests. Back off; do not tight-loop.
503UPSTREAM_UNAVAILABLEThe model provider is unavailable. Retry with backoff.

1. Get a token

Open /tokens.html in a browser and copy the token this app already holds - no developer console needed. A guest token is minted automatically and is enough for /me and /estimate; writing a package is metered and needs a personal token, which comes from signing in on that page.

Keep it in an environment variable rather than in source:

export SKILLSAFE_TOKEN="YOUR_TOKEN"

2. Check the session and the balance

GET /me is free. It returns only three fields: subject_type, subject_id and credits. Signed-in means subject_type == "user" - there is no username or email to test.

curl -sS -X GET "https://api.skillsafe.ai/v1/app-api/me" \
  -H "Authorization: Bearer $SKILLSAFE_TOKEN"

3. Price the run before making it

POST /estimate costs nothing, creates no job, and returns the worst-case cost. Compare hold_credits against the balance from step 2 before you submit: a 402 after the fact is avoidable. hold_credits is a reservation priced at the full output cap - the actual charge is usually far lower.

It also echoes model, model_alias and markup_bps, which is the authoritative check that a run is bound to the model you think it is. Estimate each lane separately: their prompts and caps differ, so their holds do.

curl -sS -X POST "https://api.skillsafe.ai/v1/app-api/estimate" \
  -H "Authorization: Bearer $SKILLSAFE_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "shot",
  "beat": "She stands still at the end of a narrow corridor, holding an opened letter, and reads the first line without moving.",
  "move": "Dolly Out",
  "seconds": 8,
  "shot_type": "reaction",
  "from_framing": "medium close",
  "to_framing": "wide",
  "travel": "2 metres",
  "look": "single cold overhead practical, no fill"
}'

4. Write a package

POST /run submits the job. Always send an Idempotency-Key: a network blip that replays the same request must not bill twice. A replay with the same key returns the stored result and is not charged again; a replay with the same key but a different body is a 409.

The response carries output.output (the Markdown package), charged_credits and truncated. If truncated is true the balance sat between min_credits and hold_credits and the output was cut short - render what arrived and say so rather than presenting it as complete.

curl -sS -X POST "https://api.skillsafe.ai/v1/app-api/run" \
  -H "Authorization: Bearer $SKILLSAFE_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "task": "shot",
  "beat": "She stands still at the end of a narrow corridor, holding an opened letter, and reads the first line without moving.",
  "move": "Dolly Out",
  "seconds": 8,
  "shot_type": "reaction",
  "from_framing": "medium close",
  "to_framing": "wide",
  "travel": "2 metres",
  "look": "single cold overhead practical, no fill"
}'

5. Stream a run

POST /run-stream is the same call with a text/event-stream response. Worth knowing before you build on it: from a server or from cURL you get event: delta frames carrying the output token by token; from a browser you get event: tick heartbeats and then one event: done with the whole output. Handle both, and treat ticks as liveness rather than progress.

Frame types are job (the job id), delta ({"text": "..."}), tick ({"t": seconds}), done, and error. An idempotent replay returns plain JSON with no stream at all, so check the content type before you start reading frames.

curl -sS -N -X POST "https://api.skillsafe.ai/v1/app-api/run-stream" \
  -H "Authorization: Bearer $SKILLSAFE_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -H "Idempotency-Key: cmd-$(date +%s)" \
  -d '{
  "task": "shot",
  "beat": "She stands still at the end of a narrow corridor, holding an opened letter, and reads the first line without moving.",
  "move": "Dolly Out",
  "seconds": 8,
  "shot_type": "reaction",
  "from_framing": "medium close",
  "to_framing": "wide",
  "travel": "2 metres",
  "look": "single cold overhead practical, no fill"
}'

6. Read the result

output.output is Markdown in the envelope this app's system prompt guarantees: every section is a level-two heading spelled exactly as listed in the lane table above, in that order; tables are GitHub pipe tables with the declared columns; prompts are in fenced blocks opened with three backticks and the word text; checklists are - [x] lines.

So parsing is a split on /^## / - but do it fence-aware, because a prompt block can legitimately contain a line starting with ##. Count the sections you got against the ones the lane declares: a short list means the run was truncated, not that the contract changed.

def sections(md):
    out, name, buf, fence = {}, None, [], False
    for line in md.split("\n"):
        if line.lstrip().startswith("```"):
            fence = not fence
        if not fence and line.startswith("## "):
            if name:
                out[name] = "\n".join(buf).strip()
            name, buf = line[3:].strip(), []
            continue
        if name:
            buf.append(line)
    if name:
        out[name] = "\n".join(buf).strip()
    return out

The artifact most callers want is the fenced text block inside ## Shot Prompt, ## Performance Prompt or ## Revised Prompt - that is what you paste into the platform. The tables beside it are the reasoning, not the deliverable.

Rate limits and good manners